Estimation for possibly non-stationary long memory processes


Time series data is increasingly prevalent, however common time series models can fail to capture the complex dynamics of time series data in practice. In this talk, we focus on a specific model — the ARFIMA(p,d,q) model — and the assumption of stationarity. Assuming that a process is stationary is technically convenient, but may not be appropriate in practice. In this paper, we introduce a likelihood-based approach to estimating the parameters of the popular ARFIMA(p,d,q) model without assuming stationarity. This allows us to implement likelihood-based tests of stationarity and to obtain better estimates of the differencing parameter d.

Maryclare Griffin
Speaker Title
Assistant Professor, Mathematics & Statistics
Speaker Institution
University of Massachusetts Amherst
Speaker Biography

Maryclare Griffin is an assistant professor of statistics at UMass Amherst. She received a PhD in statistics from the University of Washington in Seattle in 2018 and recently completed a short postdoc at Cornell University. Her research interests include high dimensional regression problems, mixed models, and methods for spatio-temporal data.